Identifying Regulatory Subnetworks for a Set of Genes

Theodore Perkins,Michelle S. Scott,François Pepin,Michael Hallett,David Y. Thomas,Scott Bunnell

doi:10.1074/mcp.m400110-mcp200

Abstract

High throughput genomic/proteomic strategies, such as microarray studies, drug screens, and genetic screens, often produce a list of genes that are believed to be important for one or more reasons. Unfortunately it is often difficult to discern meaningful biological relationships from such lists. This study presents a new bioinformatic approach that can be used to identify regulatory subnetworks for lists of significant genes or proteins. We demonstrate the utility of this approach using an interaction network for yeast constructed from BIND, TRANSFAC, SCPD, and chromatin immunoprecipitation (ChIP)-Chip data bases and lists of genes from well known metabolic pathways or differential expression experiments. The approach accurately rediscovers known regulatory elements of the heat shock response as well as the gluconeogenesis, galactose, glycolysis, and glucose fermentation pathways in yeast. We also find evidence supporting a previous conjecture that approximately half of the enzymes in a metabolic pathway are transcriptionally co-regulated. Finally we demonstrate a previously unknown connection between GAL80 and the diauxic shift in yeast.

Highlights

High throughput genomic/proteomic strategies, such as microarray studies, drug screens, and genetic screens, often produce a list of genes that are believed to be important for one or more reasons
We noticed that in the experiments where GAL80 is deleted and in the absence of galactose a surprisingly large set of genes is highly overexpressed, and many of these genes are involved in sugar metabolism
We use the 20 microarray results from Ideker et al [14] containing wild-type and single deletions of GAL genes, 10 metabolic pathways from Saccharomyces Genome Database [25], and an interaction network consisting of data from only TRANSFAC, ChIPChip, and SCPD (BIND is excluded from this analysis because we want to only look at transcriptional regulation)

Summary

Identifying Regulatory Subnetworks for a Set of Genes*

Whereas the Ideker et al [2] approach is a global search of the interaction network for differentially expressed subnetworks, our approach allows the user to specify a “distinguished” set of nodes This distinguished set might, for example, represent a list of differentially expressed genes from a microarray experiment, chemical-genetic/synthetic-lethal interactions [11], or any other type of assay that produces lists of interesting genes/proteins and where similar questions regarding regulatory relationships are important. It is likely impossible to infer a complete causal explanation of how/why a gene is important from an interaction network, the approach does produce subnetworks that are typically of a size that allows investigators to perform a more detailed, literature-based analysis This data reduction technique is analogous to a BLAST search for a given uncharacterized sequence where the goal is to find a relatively small set of likely homologues within a large data base of sequences. We present evidence in support of a claim of Ihmels et al [17] that approximately half of the enzymes in a metabolic pathway are transcriptionally co-regulated and show that our framework accurately rediscovers known regulatory elements of the gluconeogenesis, galactose, glycolysis, and glucose fermentation pathways in yeast

EXPERIMENTAL PROCEDURES

RESULTS

TABLE III Steiner transcriptional analysis of metabolic pathways

Lysine biosynthesis

DISCUSSION